System for optimizing drink blends

ABSTRACT

A system for optimizing blending. The system can include a processor configured to aggregate material information, aggregate production information, model consumer liking of the at least one product, and provide plan information for controlling production resources based on the material information, the production information, and the consumer liking. The material information can be associated with a product input of the at least one product. The production information can be associated with the production resources of the at least one product.

RELATED APPLICATIONS

This application is a continuation of and claims priority under 35U.S.C. § 120 to U.S. patent application Ser. No. 14/148,513 entitled“System For Optimizing Drink Blends”, filed Jan. 6, 2014, issued as U.S.Pat. No. 10,261,501 on Apr. 16, 2019, which is a continuation of U.S.patent application Ser. No. 12/940,173 entitled “System for OptimizingDrink Blends,” filed Nov. 5, 2010, which issued as U.S. Pat. No.8,626,327 on Jan. 7, 2014, U.S. patent application Ser. No. 12/940,182entitled “System for Modeling Drink Supply and Demand,” filed Nov. 5,2010, which is now abandoned, U.S. patent application Ser. No.12/940,205 entitled “Total Quality Management System for OptimizingDrink Process,” filed Nov. 5, 2010, which is now abandoned, U.S. patentapplication Ser. No. 12/940,222, entitled “Drink Production ProcessSimulator,” filed Nov. 5, 2010, issued as U.S. Pat. No. 8,626,564 onJan. 7, 2014, and U.S. patent application Ser. No. 12/940,195 entitled“System for Targeting Promotions Based on Input and Production,” filedNov. 5, 2010, which is now abandoned; the disclosures of all the abovelisted applications are incorporated herein by reference for allpurposes.

BACKGROUND

The following description is provided to assist the understanding of thereader. None of the information provided or references cited is admittedto be prior art.

Developing a production plan for fruit-based drinks presents uniquechallenges for a business unit manager. The input material forfruit-based drinks, for example, fruit, can be highly variable inavailable quantity, cost, and quality. For example, available quantity,cost, and quality of fruit can depend on hurricane activity or if anearly freeze occurs. Thus, the supply of available fruit for drinks canbe highly variable across multiple dimensions.

Consumer demand for fruit-based drinks can depend on quality and cost.For example, consumers might buy more of a fruit-based drink when thedrink composition is sweeter. However, the available quantity of sweetfruit may be limited. Thus, the business unit manager, faced with alimited quantity of sweet fruit, must develop a production plan thatbalances the available fruit with the perceived consumer demand.

The business unit manager has limited information and must relyprimarily on intuition to develop a production plan. An intuitiveapproach can result in inconsistent results over time as well as acrossregions. Further, when a business unit manager leaves or moves to adifferent position, the institutional knowledge and experience of thatperson is lost. Therefore, improved systems and methods for developingand implementing production plans for fruit-based drinks are needed.

SUMMARY

One illustrative embodiment relates to a system for optimizing blending.The system can include a processor configured to aggregate materialinformation, aggregate production information, model consumer liking ofat least one product, and provide plan information for controllingproduction resources based on the material information, the productioninformation, and the consumer liking. The material information can beassociated with a product input of the at least one product. Theproduction information can be associated with production resources ofthe at least one product.

Another illustrative embodiment relates to a method of optimizingblending. Material information can be aggregated at a processor. Thematerial information can be associated with a product input of at leastone product. Production information can be aggregated. The productioninformation can be associated with production resources of the at leastone product. Consumer liking of the at least one product can be modeled.Plan information for controlling the production resources can beprovided based on the material information, the production information,and the consumer liking.

Another illustrative embodiment relates to an article of manufactureincluding a tangible computer-readable medium having instructions storedthereon that, if executed by a computing device, cause the computingdevice to perform operations. The operations can include aggregatingmaterial information, aggregating production information of the at leastone product, modeling consumer liking of at least one product, andproviding plan information for controlling the production resourcesbased on the material information, the production information, and theconsumer liking. The material information can be associated with aproduct input of the at least one product. The production informationcan be associated with production resources of the at least one product.

BRIEF DESCRIPTION OF THE DRAWINGS

The foregoing and other features of the present disclosure will becomemore fully apparent from the following description and appended claims,taken in conjunction with the accompanying drawings. Understanding thatthese drawings depict only several embodiments in accordance with thedisclosure and are, therefore, not to be considered limiting of itsscope, the disclosure will be described with additional specificity anddetail through use of the accompanying drawings.

FIG. 1 is a block diagram of a drink supply chain in accordance with anillustrative embodiment.

FIG. 2 is a schematic of a blend plan system in accordance with anillustrative embodiment.

FIG. 3 is a diagram of a blending model architecture in accordance withan illustrative embodiment.

FIG. 4 is a diagram of a constraint architecture in accordance with anillustrative embodiment.

FIG. 5 is a flowchart of operations performed by a blending plan systemin accordance with an illustrative embodiment.

FIG. 6 is a diagram of a blending model architecture in accordance withan illustrative embodiment.

FIG. 7 is a diagram of a blending model architecture in accordance withan illustrative embodiment.

FIG. 8 is a flowchart of operations performed in a branch and boundmethod in accordance with an illustrative embodiment.

FIG. 9 is a diagram of an interior point approach in accordance with anillustrative embodiment.

DETAILED DESCRIPTION OF THE ILLUSTRATIVE EMBODIMENTS

Described herein are illustrative systems, methods, computer-readablemedia, etc. for optimizing drink blends. In the following detaileddescription, reference is made to the accompanying drawings, which forma part hereof. In the drawings, similar symbols typically identifysimilar components, unless context dictates otherwise. The illustrativeembodiments described in the detailed description, drawings, and claimsare not meant to be limiting. Other embodiments may be utilized, andother changes may be made, without departing from the spirit or scope ofthe subject matter presented here. It will be readily understood thatthe aspects of the present disclosure, as generally described herein,and illustrated in the figures, can be arranged, substituted, combined,and designed in a wide variety of different configurations, all of whichare explicitly contemplated and make part of this disclosure.

Referring to FIG. 1, a block diagram of a drink supply chain 100 inaccordance with an illustrative embodiment is shown. The drink supplychain 100 can be associated with the production and distribution of adrink (i.e., beverage) such as juice not from concentrate, juice fromconcentrate, carbonated beverages (i.e., soft drinks), whole grainbeverages, coffees, teas, energy drinks, health drinks, beauty drinks,nutritional beverages, flavored water, milk, dairy drinks, kvass, breaddrinks, non-alcoholic beverages, alcoholic beverages, wine, beer,tequila, vodka, rum, or any other beverages. The drink supply chain 100can include beverage inputs 110, other inputs 120, inventory 130,production resources 140, channels 150, consumers 160, and a blend planoptimization system 170. The blend plan optimization system 170 caninteract with each of suppliers of the beverage inputs 110, suppliers ofthe other inputs 120, the inventory 130, the production resources 140,the channels 150, and the consumers 160 automatically or manually, i.e.,with some amount of human participation. This interaction may includecommunicating data to and from suppliers of the beverage inputs 110,suppliers of the other inputs 120, computers tracking the inventory 130,computers tracking the production resources 140, computers associatedwith the channels 150, and computers associated with the consumers 160.

The beverage inputs 110 can include any agricultural product such as,but not limited to, fruits, vegetables, grains, nuts, etc. In anillustrative embodiment, the beverage inputs 110 can include, but arenot limited to, acerola concentrate, acerola puree/paste, aloeveracrushed, bits, & pieces, apple concentrate, apple not from concentrate,apple puree/paste, apricot concentrate, apricot puree/paste, bananaconcentrate, banana puree/paste, beet concentrate, blackberryconcentrate, blackberry puree/paste, blackcurrant concentrate, blueberryconcentrate, blueberry puree/paste, carrot concentrate, carrot not fromconcentrate, carrot organic not from concentrate, carrot pulp, cashewconcentrate, cherry concentrate, cherry puree/paste, chokeberryconcentrate, coconut cream, cranberry concentrate, cranberry not fromconcentrate, gooseberry concentrate, grape concentrate, grape not fromconcentrate, grapefruit concentrate, grapefruit not from concentrate,grapefruit pulp, grapefruit puree/paste, guava concentrate, guavapuree/paste, kiwi concentrate, kumquat puree/paste, lemon concentrate,lemon not from concentrate, lemon pulp, lime concentrate, lime not fromconcentrate, lime pulp, lychee concentrate, mandarin concentrate, mangoconcentrate, mango puree/paste, melon concentrate, mulberry concentrate,multifruit blends concentrate, orange concentrate, orange not fromconcentrate, orange pulp, orange wesos, papaya crushed, bits, andpieces, passion fruit concentrate, passion fruit puree/paste, passionfruit unknown, peach concentrate, peach crushed, bits, and pieces, peachpuree/paste, pear concentrate, pear puree/paste, pineapple concentrate,pineapple crushed, bits, and pieces, pineapple not from concentrate,plum concentrate, plum puree/paste, pomegranate concentrate, quincypuree/paste, raspberry concentrate, raspberry puree/paste, redcurrantconcentrate, rhubarb concentrate, sourcherry concentrate, sourcherry notfrom concentrate, strawberry concentrate, strawberry crushed, bits, andpieces, strawberry puree/paste, tamarind puree/paste, tangerineconcentrate, tangerine not from concentrate, tomato concentrate, tomatopuree/paste, watermelon concentrate, yumberry concentrate, yuzuconcentrate, honey, sugars, milk, dairy products, spices, herbs, leaves,seeds, pistils, flour, wheat, barley, oats, rye, corn, quinoa and rice.For every drink or group of drinks, the beverage inputs 110 can bedifferent. In one illustrative embodiment, the beverage inputs 110 caninclude fruit. For example, the fruit can include oranges. The orangescan include, for example, Valencia oranges, early/midseason naveloranges, Brazilian oranges, or Costa Rican oranges. The crops for eachof the Valencia oranges, early/midseason navel oranges, Brazilianoranges, or Costa Rican oranges can be ready for market at varioustimes, have varying cost, quality and quantity. The beverage inputs 110can be purchased under contract or purchased on the spot market. Inanother example, the fruit can include apples or mangos. However, anyfruit, vegetable, or fruit/vegetable product or byproduct can be used.In another illustrative embodiment, the beverage inputs 110 can includestable drink components, for example, high fructose corn syrup, flavors,starch, additives, minerals, vitamins, alcohol, carbon dioxide,phosphoric acid, citric acid, artificial sweeteners, enzymes, starch,salt, gellan gum, carrageenan, cellulose gel, cellulose gum, pectin,modified food starch, agar, guar gum, xanthan gum, propylene glycolalginate, locust bean gum, gum Arabic, etc.

The other inputs 120 can include any non-food resources used to producea drink product. For example, the other inputs 120 can includecontainers, bottles, labels, water, cleaners, energy, etc. Each item inthe other inputs 120 can have different cost, availability, quantity,quality, etc.; however, items identified as the other inputs 120 cantypically be more predicable than those associated with the beverageinputs 110. The products associated with other inputs 120 can bepurchased under contract or purchased on the spot market.

The intake of beverage inputs 110 and the other inputs 120 can bemanaged with the inventory 130. The inventory 130 can include on-siteand off-site facilities. For example, inventory 130 can include in-housetank capacity and supplier tank capacity. The inventory 130 can includetanks, tank farms, warehousing, etc. The inventory 130 can have acapacity and a current stock of input materials, as discussed above. Theinventory 130 can be associated with a computer.

The inventory 130 can be used to provide input materials to theproduction resources 140 for the production of drinks. The productionresources 140 can include mixing machinery such as blending vats,bottling equipment, pasteurization equipment, packaging equipment,labor, etc. The production resources 140 can have a production capacity,operation cost, and limiting factors. Limiting factors can includeconstraints on the manufacturing process, for example, a constraintcould be that once a tank is opened the entirety should be used. Anotherexample of a constraint could be that when a production line is changedover, the production line should be sanitized. The production resources140 can be used to produce finished units of drink product. Multipledrink products can be produced using the same production resources 140.Each drink product has a profile based on a number of attributes,discussed further below. The finished units of drink product can be inthe form of various store keeping units (SKUs).

The channels 150 can be used to distribute the finished units of drinkproduct to the consumers 160. The channels 150 can includetransportation resources, warehousing, wholesale distributors, andretail outlets. The consumers 160 can purchase the finished units ofdrink product through the channels 150. A consumer liking for each drinkproduct can be predicted based on the respective profile of each drinkproduct compared to customer surveys and actual purchase data. Theliking of each drink product can be used to predict a demand for eachdrink product. The consumers 160 can also provide data to the blend planoptimization system 170 via computer enabled surveys or manually-enteredsurvey data.

The blend plan optimization system 170 can receive and send data fromcomputers associated with suppliers of the beverage inputs 110,computers associated with suppliers of other inputs 120, computersassociated with the inventory 130, computers associated with theproduction resources 140, computers associated with the channels 150,and computers associated with the consumers 160. The blend planoptimization system 170 can use data describing the beverage inputs 110,the other inputs 120, the inventory 130, the production resources 140,the channels 150, and the consumers 160 to determine, for example,possible blend plans. The blend plan optimization system 170 canoptimize the blend plan to maximize or minimize multiple attributes ofthe blend plans, as discussed further below. In one illustrativeembodiment, the blend plan optimization system 170 can predict demand ofthe consumers 160, obtain orders from channels 150, order the beverageinputs 110 and the other inputs 120, manage the inventory 130, andcontrol the production resources 140. In other illustrative embodiments,the blend plan optimization system 170 can provide planning andoperating information for users such as business unit managers.Advantageously, the blend plan optimization system 170 can provide costand quality data and a common communication platform to enablecross-functional coordination to enhance blending decisions.Advantageously, the blend plan optimization system 170 can efficientlyanalyze multiple scenarios, vary demand, raw material attributes, andcosts at a granular level to evaluate trade-offs and execute strategy.

Referring to FIG. 2, a schematic of a blend plan system 200 inaccordance with an illustrative embodiment is shown. The blend plansystem 200 can include a computing device 210, an input materialdatabase 220, an inventory database 225, a production database 230, achannel database 235, a consumer database 240, and a network 245. Thecomputing device 210 can communicate with the input material database220, the inventory database 225, the production database 230, thechannel database 235, the consumer database 240, and the network 245.The computing device 210 can use the network 245 to communicate withother databases, suppliers, plants, storage facilities, machinery,distributors, and consumers.

Computing device 210 can include a desktop computer, a laptop computer,a cloud computing client, a hand-held computing device, or other type ofcomputing device known to those of skill in the art. Computing device210 can include a processor 250, a memory 260, a user interface 270, adisplay 280, blending model software 290, and transceiver 295. Inalternative embodiments, computing device 210 may include fewer,additional, and/or different components. Memory 260, which can includeany type of permanent or removable computer memory known to those ofskill in the art, can be a computer-readable storage medium. Memory 260can be configured to store blending model software 290 and anapplication configured to run the blending model software 290, captureddata, and/or other information and applications as known to those ofskill in the art. Transceiver 295 of computing device 210 can be used toreceive and/or transmit information through a wired or wireless networkas known to those skill in the art. Transceiver 295, which can include areceiver and/or a transmitter, can be a modem or other communicationcomponent known to those of skill in the art.

The blending model software 290 can be configured to analyze data fromthe input material database 220, the inventory database 225, theproduction database 230, the channel database 235, the consumer database240, and the network 245 to form at least one blend plan. The data canbe received by computing device 210 through a wired connection such as aUSB cable and/or through a wireless connection, depending on theembodiment. The blending model software 290, which can be implemented ascomputer-readable instructions configured to be stored on memory 260,can optimize the at least one blend plan for all attributessimultaneously or a particular attribute.

In one embodiment, the blending model software 290 can include acomputer program and/or an application configured to execute the programsuch as Cplex optimizing software available from International BusinessMachines, Inc., Armonk, N.Y. Alternatively, other programming languagesand/or applications known to those of skill in the art can be used. Inone embodiment, the blending model software 290 can be a dedicatedstandalone application. Processor 250, which can be in electricalcommunication with each of the components of computing device 210, canbe used to run the application and to execute the instructions of theblending model software 290. Any type of computer processor(s) known tothose of skill in the art may be used.

The input material database 220 can include data on beverage inputs,such as agricultural inputs, and other inputs for drink blending andpackaging. Data on agricultural inputs can include attribute data forexpected shipments of agricultural inputs. For example, attribute datacan include information about amount, cost, timing, brix, citric acid,brix acid ratio, vitamin c (ascorbic acid), color score, viscosity,limonin, flavor, fruit variety (such as early/mid navel oranges orValencia oranges), and a pulp profile of an expected or contractedshipment. Data on other inputs can include information about packagingmaterials available or expected to be available, sweeteners available,water quality, etc.

The inventory database 225 can include data on currently held beverageinputs, such as agricultural inputs, and other inputs. The data caninclude internal information and information about suppliers. When ashipment of an agricultural input arrives at a storage facility orplant, the agricultural input can be tested to determine attribute data.For example, tested attribute data can include information about amount,brix, citric acid, brix acid ratio, vitamin c (ascorbic acid), colorscore, viscosity, limonin, flavor, fruit variety, and pulp profile of areceived shipment. In one illustrative embodiment, the data includes theamount of juice stored in a tank. The collected data can be stored inthe inventory database 225. The data can also include information aboutthe cost of inventory and timing of deliveries and planned use. Theinventory database 225 can also include quantity, cost, and typeinformation for other inputs such as packaging.

The production database 230 can include data on current productionresources that are available for use. Production resources can include,for example, plants, storage facilities, and machinery. The data caninclude information about location, shipping costs, machine capacities,machine capability and machine schedules. The data can also includeinformation about how resources are linked or related. For example,tanks ‘A’ and ‘B’ might only be piped to machine ‘W.’ Illustrativemachinery can include blending tanks, pasteurizing equipment, andbottling machines.

The channel database 235 can include data on channels used to distributefinished product. For example, data on channels can include historicaldata for demand through a particular channel. Data can includeinformation about finished inventory on hand in a particular channel.Data can also include information about the timing and requirements ofcontract shipments to, for example, restaurants and food servicecompanies.

The consumer database 240 can include data on consumer demand. Forexample, consumer demand data can include consumer survey results andsales results. The consumer survey results and sales results can be usedto build customer liking and demand models.

Referring to FIG. 3, a diagram of a blending model architecture 300 inaccordance with an illustrative embodiment is shown. The blending modelarchitecture 300 can include a blending model 360. Inputs to theblending model 360 can include forecasts 310, inventory information 320,production information 330, channel information 340, and desiredattributes 350. The blending model 360 generates a blending plan 365 andan optimal solution 367 based on the inputs 310-350. A liking profiler370 provides a liking profile 375 for the blending plan 365. Theblending plan 365 and its optimal solution 367 and the liking profile375 can then be stored in a database 380 for further analysis. Althougha blending model for juice is described, the blending model can beapplied to any agriculture-based product. The blending model can beapplied to juice not from concentrate, juice from concentrate,carbonated beverages (i.e., soft drinks), whole grain beverages,coffees, teas, energy drinks, health drinks, nutritional beverages,beauty drinks, flavored water, milk, dairy drinks, kvass, bread drinks,non-alcoholic beverages, alcoholic beverages, wine, beer, tequila,vodka, rum, or any other beverages.

The forecasts 310 can include a juice forecast, a demand forecast, aninventory forecast, a production availability forecast, a fruitforecast, a vegetable forecast, a nut forecast, a grain forecast, acommodity forecast, or any other forecast. For example, the juiceforecast can include an estimate of when, where, and how much fruitjuice will be available for blending. The forecasts 310 can includematerial information such as data about beverage inputs, such asagricultural inputs, and other inputs. Forecasted attributes can includequantity, brix, citric acid, brix acid ratio, centrifuge pulp profile,vitamin c (ascorbic acid), percent recovered oil, color score, defectsscore, limonin, flavor, and varietal percentages (Brazilian, Early Mid,and Valencia). The pulp profile consists of information about thedistribution of pulp lengths in the expected juice. The forecasts 310can be provided manually or generated based on known and historicalinformation stored in an input material database.

The inventory information 320 includes data on materials currentlyavailable for blending. For example, the inventory information 320 caninclude where and how much fruit juice is currently available forblending. Inventory attributes can include quantity, age of juice, brix,citric acid, brix acid ratio, centrifuge pulp profile, vitamin c(ascorbic acid), percent recovered oil, color score, defects score,limonin, flavor, and varietal percentages (Brazilian, Early Mid, andValencia). The inventory information 320 can be obtained from aninventory database.

The production information 330 includes data on the available productionresources. For example, production information 330 can include dataabout available resources such as storage capacity, storage costs,production capacity, and production costs. The production information330 can be obtained from a production database.

The channel information 340 includes data about the wholesalers andretailers such as finished inventory on hand in a particular channel.Data can also include information about the timing and requirements ofcontract shipments to, for example, restaurants and food servicecompanies. The channel information 340 can be obtained from a channeldatabase.

The desired attributes 350 can include a series of constraints.Referring to FIG. 4, a diagram of a constraint architecture 400 inaccordance with an illustrative embodiment is shown. The constraintarchitecture 400 can be categorized into primary constraints 410,business restrictions 420, and miscellaneous constraints 430. Theconstraints and restrictions can define, for example, bounds, limits,conditions, and undesired configurations for blend plans. More or fewerconstraints and restrictions can be implemented.

The primary constraints 410 can include flow balance, sourcing bounds,quality bounds 440, demand, tank capacity, pasteurization capacity,varietal, load-out capacity, fresh vs. stored, juice age, and minimumsupply requirement. Flow balance can constrain the model to ensure thatflow into the system equals flow out of the system plus inventory.Sourcing bounds can define minimum and maximum purchases from suppliers.For example, a supplier may have a maximum capacity.

Quality bounds 440 can define the attributes of multiple finishedproducts. Quality bounds 440 can include brix, citric acid, brix acidratio, centrifuge pulp profile, vitamin c (ascorbic acid), percentrecovered oil, color score, defects score, limonin, and flavor score.The quality bounds 440 can set minimum levels, maximum levels, orminimum and maximum ranges for an attribute. Each product or SKU canhave a separate set of quality bounds 440.

Demand can set bounds for amount of finished product to be produced.Tank farm capacity can define the maximum amount of tank capacityavailable for a particular time period, for example, a week, a month,etc. Pasteurization capacity can define the maximum amount of juice thatcan be pasteurized at a plant. Varietal can define the bounds for theratio of early/midseason fruit to Valencia fruit to foreign fruits usedin a product. Load-out capacity can define the maximum ability of alocation to ship out product. Fresh vs. stored can define the bounds forthe ratio of the amount fresh juice in a product to the amount storedjuice in the product. Juice age can define that maximum amount of timejuice can sit in a tank before the juice should be used. Minimum supplyrequirements can define the minimum amount supply purchases that must bemade because of, for example, a contract.

Business restrictions 420 can include, for example, minimum own sourcingrequirements, tank available period restriction, minimum carry-overrequirement, in-season new stored restriction, prohibited flows,consumption requirements, and minimum ending inventory requirement.Minimum own sourcing requirements can define the minimum amount of fruitor juice that should be sourced internally, i.e., from farms owned bythe company. The tank available period restriction can define timeperiods for which particular tanks are available, i.e., not scheduledfor use. The minimum carry-over requirement can define how much inputinventory should be maintained at the end of a production plan period.The in-season new stored restriction can define how much new fruit juicecan be stored in tanks. Prohibited flows define restrictions on processflows such as moving stored juice from a first plant to a second plant.Consumption requirements can define how soon particular inputs should beused after being received. For example, a consumption requirement can bethat all foreign-sourced fruit should be consumed within a certain timeperiod, for example, within a week of reception. The minimum endinginventory requirement can define how much stock on hand that should bemaintained for a product.

Miscellaneous constraints 430 can include situational or testconstraints. For example, miscellaneous constraints 430 can include thatflow of new stored juice from tank to plant is blocked. Any otherconstraint can also be included.

Referring again to FIG. 3, the blending model 360 can include objectivefunctions 361 and constraint functions 362. The objective functions 361and the constraint functions 362 can be a system of linear equations.The blending model 360 can be a constraint program with a mix ofcontinuous and integer variables with some logical constraints. Theblending model 360 can iteratively find solutions for objectivefunctions 361 and the constraint functions 362 using various techniquessuch as interior point methods. For example, the system of linearequations can be solved using Cplex optimizing software available fromInternational Business Machines, Inc., Armonk, N.Y. A range of possiblesolutions can be produced. The possible solutions for objectivefunctions 361 and the constraint functions 362 are valid blending plans365. The blending plans 365 are plans that define the inputs to use,resources to use, and products to be made.

The objective functions 361 define the objectives of the analysis. Inone illustrative embodiment, the objective can be to minimize cost.However, any objective is possible including maximizing quality, orminimizing carbon footprint. The objective functions 361 can alsoinclude secondary and tertiary objectives, etc. The solution for theobjective functions 361 can be the optimal solution 367. The optimalsolution 367 can be the set of variable values, decisions, andassociated objective function value that maximizes and/or minimizes theobjective function, subject to the constraints. For example, the optimalsolution 367 can be the minimized cost calculated for a particularblending plan 365.

In one illustrative embodiment, the objective functions 361 includeexpressions that represent the various costs involved in producingjuice. For example, objective functions 361 can include a process costexpression, a storage cost expression, and a transportation costexpression. For example, for a particular supplier, plant, andtransport, the expression can be: cost=supply cost pergallon*gallons+production cost per gallon*gallons+transportation costper gallon*gallons. The sum of these expressions can equal the totalcost of producing and delivering finished juice products. In oneillustrative embodiment, the total cost of producing and deliveringfinished juice products is the optimal solution 367. The objectivefunctions 361 can also include preference terms and penalties. Forexample, penalties can include a fresh juice penalty, an overproductionpenalty, and a flow penalty. In one example, the preference terms arecounted as reduced costs and penalties are counted as increased costs.

The possible solutions for the objective functions 361 are limited byvalid combinations defined by the constraint functions 362. Anillustrative constraint function for brix of the finished product, canbe: 10<brix<14. Thus, only blending plans 365 where the finished producthas a brix greater than 10 but less than 14 are valid blending plans365. Another illustrative constraint function for pulp profile can be: 2grams/deciliter<fine pulp (<2 mm)<4 grams/deciliter; 5grams/deciliter<medium pulp (2-5 mm)<8 grams/deciliter; 2grams/deciliter<large pulp (>5 mm)<4 grams/deciliter. Thus, onlyblending plans 365 where the finished product has a pulp profile thatfits within these pulp profile constraints are valid blending plans 365.

The blending model 360 can be executed using a branch and bound methodand/or an interior point method. In an illustrative embodiment, theobjective functions 361 and constraint functions 362 can be combined toform a math program. The math program can optimize the objectivefunctions 361 subject to the constraint functions 362. After an optimalblending plan 365 is determined in view of the constraint functions 362,inputs to the model can be changed to evaluate different scenarios. Theoptimal solution 367 for the objective functions 361, as well as therespective blending plan 365, can be stored for further analysis. Afterthe objective functions 361 have been solved for a number of validblending plans 365 (resulting in a respective number of optimalsolutions 367), the best optimal solution 367 (e.g., minimum costsolution) can be selected. The granularity of the blending plans 365 canbe changed to increase the execution speed or increase the precision ofthe blending model 360. Alternatively, the blending model 360 can beexecuted using a Monte Carlo-type methodology.

Referring to FIG. 8, a flowchart of operations performed in a branch andbound method 800 in accordance with an illustrative embodiment is shown.Additional, fewer or different operations may be performed. In anoperation 810, a continuous relaxed problem (X(i)=a) can be defined.

The integer requirements of the continuous relaxed problem are relaxedand the continuous relaxed problem can be solved as a continuousvariable problem. This relaxed problem can be solved using an interiorpoint algorithm, or a gradient descent algorithm. A variable (X(i)) canselected to ‘branch’ on based on the partial derivative of the objectivefunction, projected on to the constraint surface, with respect to thevariable. In an operation 820, along one branch the branching variableis constrained to be less than or equal to the next lowest integervalue, e.g., sub problem X(i)<=b. In an operation 830, along the otherbranch the branching variable is constrained to be greater than or equalto the next highest value, e.g., sub problem X(i)>=c. The resultingsub-problems are solved until an optimal solution is found that obeysall constraints and integrality requirements. A branch and cut algorithmcan also be used, and branch and bound and branch and cut can be used incombination.

Referring to FIG. 9, a diagram of an interior point approach 900 inaccordance with an illustrative embodiment is shown. In an illustrativeembodiment, the interior point approach 900 is a two dimensional linearinteger program. In the interior point approach 900, constraintfunctions 962 are projected onto integer points 910. In an illustrativeembodiment, all integer points 910 located above constraint functions962 are not valid solutions. An objective function 961 can be optimized(i.e., re-plotted in the direction of the arrow) until only one of theinteger points 910 below the constraint functions 962 remains above theobjective function 961. In FIG. 9, an optimal solution 967 is the lastinteger point below the constraint functions 962 that remains above theobjective function 961. Thus, the optimal solution 967 maximizes theobjective function 961 while remaining within the bounds of theconstraint functions 962. In other embodiments, multiple dimensions andmultiple objective functions can be used.

Referring again to FIG. 3, each valid blending plan 365 can be processedby the liking profiler 370. The liking profiler 370 can be a model ofconsumer liking based on the attributes of a product. In oneillustrative embodiment, the liking profiler 370 can be amulti-dimensional mathematical model that associates a liking score withthe brix, citric acid, brix acid ratio, centrifuge pulp profile, vitaminc (ascorbic acid), percent recovered oil, color score, defects score,limonin, and flavor score of a product. More or fewer attributes can beincluded. The attributes can be weighted. The liking score can be arelative value. The liking score can be a scalar, vector, or randomvariable. The multi-dimensional mathematical model can be populated withdata from consumer surveys and consumer purchase information. The datafrom consumer surveys and data from consumer purchase information can bestored in a consumer database. For example, in a consumer survey, aconsumer is given a product to try where the product has knownattributes. The consumer can complete a consumer survey rating variousfeelings toward the product. For example, the consumer can rate themouth feel of the product on a scale of one to ten. A statistical modelcan be constructed using the responses of multiple consumers. Similarly,a statistical model can be constructed using product purchasinginformation matched with product attributes. The multi-dimensionalmathematical model is a compilation of these data describing consumers.For a given brix, citric acid, brix acid ratio, centrifuge pulp profile,vitamin c (ascorbic acid), percent recovered oil, color score, defectsscore, limonin, and flavor score of a product, the multi-dimensionalmathematical model can produce a liking score. The liking profiler 370will score each product in the blending plan 365. The liking profiler370 returns the liking profile 375 which can consist of the liking scorefor each product in the blending plan 365. Alternatively, the likingprofiler 370 can return a liking score for each SKU.

The blending plan 365 and its optimal solution 367 and liking profile375 can be stored in a database 380 for display or further analysis. Theresults of the analysis can be interactively displayed. For example, agraph of blending plans 365 showing cost (i.e., optimal solution 367)versus liking (i.e. liking profile 375) can be presented to a user suchas a business unit manager. The user can also change the variousattributes, constraints, cost structures and resources available tosimulate how changes will effect the drink production system.Sensitivity analyses can include automatically generating scenarioswhere attributes, constraints, cost structures and resources are changedby a percentage, for example, ten percent.

In addition, the variability of the attributes, constraints, coststructures and resources can be tracked over time and/or simulated anddisplayed for analysis. Since the inputs to the drink production processcan have a large variance, it can be difficult for managers to identifysections of the drink production process that are poorly controlled. Inan illustrative embodiment, the constraint functions 362 can includeproduction limits that establish operating tolerances of the productionresources. In another illustrative embodiment, the objective functions361 can include minimizing variance in one or more of the forecasts 310,the inventory information 320, the production information 330, thechannel information 340, and the desired attributes 350. By simulatingvarious production scenarios, managers can identify high variabilitysections of the drink production process. Further, the drink productionsystem can determine and track the variability in a section of the drinkproduction process based on the variability of the inputs to theparticular section of the drink production process. Thus, a manager candifferentiate a section of the drink production process that isnecessarily variable from a section of the drink production process thatis out of control and needs improvement. Further, the manager cansimulate how the process would change if variability is reduced in aparticular process section. For example, the manager could determinethat 1% improvement in the variance of machine changeover could resultin a 3% increase in process throughput. Thus, the manager could focus onimproving changeover performance.

Advantageously, the blending model architecture can provide cost andquality data and a common communication platform to enablecross-functional coordination to enhance blending decisions.Advantageously, the blending model architecture can efficiently analyzemultiple scenarios, vary demand, raw material attributes, and costs at agranular level to evaluate tradeoffs and execute strategy.Advantageously, users can interact with various blending plans 365 tobetter understand possible blending plans 365 that meet businessobjectives.

Referring to FIG. 5, a flowchart of operations performed by a blendingplan system 500 in accordance with an illustrative embodiment is shown.Additional, fewer or different operations may be performed. In anoperation 510, input material data can be aggregated by the blendingplan system. The blending plan system can query an input materialdatabase for data on beverage inputs, such as agricultural inputs, andother inputs for drink blending and packaging. Data on agriculturalinputs can include attribute data for expected shipments of agriculturalinputs. For example, attribute data can include information aboutamount, cost, timing, brix, citric acid, brix acid ratio, vitamin c(ascorbic acid), color score, viscosity, limonin, flavor, fruit variety(such as early/mid navel oranges or Valencia oranges), and pulp profileof an expected or contracted shipment. Data on other inputs can includeinformation about packaging materials available or expected to beavailable, sweeteners available, water quality, etc. The input materialdata can include a forecast.

In an operation 520, inventory data can be aggregated by the blendingplan system. The blending plan system can query an inventory databasefor data on currently held beverage inputs, such as agricultural inputs,and other inputs. The data can include internal information andinformation about suppliers. For example, attribute data can includeinformation about amount, cost, timing, brix, citric acid, brix acidratio, vitamin c (ascorbic acid), color score, viscosity, limonin,flavor, fruit variety, and pulp profile of a received shipment. In oneillustrative embodiment, the data includes the amount of juice stored ina tank. The data can also include quantity, cost, and type informationfor other inputs such as packaging.

In an operation 530, production data can be aggregated by the blendingplan system. The blending plan system can query a production databasefor data on current production resources that are available for use.Production resources can include, for example, plants, storagefacilities, and machinery. The data can include information aboutlocation, shipping costs, machine capacities, machine capability andmachine schedules. The data can also include information about howresources are linked and related. Illustrative machinery can includeblending tanks, pasteurizing equipment, and bottling machines.

In an operation 540, channel data can be aggregated by the blending plansystem. The blending plan system can query a channel database for dataon channels used to distribute finished product. For example, data onchannels can include historical data for demand through a particularchannel. Data can include information about finished inventory on handin a particular channel. Data can also include information about thetiming and requirements of contract shipments to, for example,restaurants and food service companies.

In an operation 550, the blending plan system can generate constraintfunctions based on the aggregated input data, the aggregated inventorydata, the aggregated production data, and the aggregated channel data,as described above. The constraint functions limit valid blending plansto the available and potential inputs, inventory, production resources,and channel resources. The constraint functions also limit validblending plans to desired product attributes and operationalconstraints.

In an operation 560, the blending plan system can generate objectivefunctions based on a desired objective, as described above. For example,a desired objective can be minimizing cost. Objective functions can alsoinclude secondary and tertiary objectives, for example, maximizingquality.

In an operation 570, the blending plan system can execute a blendingmodel to produce blending plans and optimal solutions based on theconstraint functions and objective functions, as described above. Theobjective functions and the constraint functions can be a system oflinear equations. The blending model can be a constraint program with amix of continuous and integer variables with some logical constraints.The blending model can iteratively find solutions for the objectivefunctions and the constraint functions using various techniques such asinterior point methods. For example, the system of linear equations canbe solved using Cplex optimizing software available from InternationalBusiness Machines, Inc., Armonk, N.Y. A range of possible solutions canbe produced. The possible solutions for objective functions and theconstraint functions are blending plans. The blending plans are plansthat define the inputs to use, resources to use, and products to bemade.

In an operation 580, the blending plan system can generate a likingprofile for each blending plan, as described above. The liking profilecan be determined using a model of consumer liking based on theattributes of a product. In one illustrative embodiment, amultidimensional mathematical model that associates a liking score withthe brix, citric acid, brix acid ratio, centrifuge pulp profile, vitaminc (ascorbic acid), percent recovered oil, color score, defects score,limonin, and flavor score of a product can be used to generate a likingprofile. More or fewer attributes can be included. The liking profilecan consist of the liking score for each product in the blending plan.Alternatively, the liking profile can consist of the liking score foreach SKU in the blending plan.

In an operation 590, the blending plan system can store the blendingplan and its optimal solution and liking profile in a database forimplementation, display or further analysis. In an operation 595, theblending plan results and related analysis can be interactivelydisplayed. For example, a graph of blending plans showing cost (i.e.,optimal solution) versus liking (i.e. liking profile) can be presentedto a user such as a business unit manager. The user can also change thevarious attributes, constraints, cost structures and resources availableto simulate how changes will effect the drink production system.Sensitivity analyses can include automatically generating scenarioswhere attributes, constraints, cost structures and resources are changedby a percentage, for example, ten percent.

In addition, the variability of the attributes, constraints, coststructures and resources can be tracked over time and/or simulated anddisplayed for analysis. Since the inputs to the drink production processcan have a large variance, it can be difficult for managers to identifysections of the drink production process that are poorly controlled. Bysimulating various production scenarios, managers can identify highvariability sections of the drink production process. Further, the drinkproduction system can determine and track the variability in a sectionof the drink production process based on the variability of the inputsto the particular section of the drink production process. Thus, amanager can differentiate a section of the drink production process thatis necessarily variable from a section of the drink production processthat is out of control and needs improvement. Alternatively, the blendplan system can use the blending plans order material inputs, manageinventory, and control the production resources such as mixingmachinery.

Advantageously, the blending model system can provide cost and qualitydata and a common communication platform to enable cross-functionalcoordination to enhance blending decisions. Advantageously, the blendingmodel system can efficiently analyze multiple scenarios, vary demand,raw material attributes, and costs at a granular level to evaluatetradeoffs and execute strategy. Advantageously, users can interact withvarious blending plans to better understand possible blending plans thatmeet business objectives.

Referring to FIG. 6, a diagram of a blending model architecture 600 inaccordance with an illustrative embodiment is shown. The blending modelarchitecture 600 can include a blending model 660, as discussed above.Inputs to the blending model 660 can include a forecast, inventoryinformation, production information, channel information, and desiredattributes, as described above. The blending model 660 generates ablending plan 665 and an optimal solution 667 based on the inputs. Aliking profiler 670 provides a liking profile 675 for the blending plan665. The blending plan 665 and its optimal solution 667 and likingprofile 675 can then be provided to a demand module 690. The demandmodule 690 can generate a demand profile 695 for the blending plan 665.The blending plan 665 and its optimal solution 667, liking profile 675,and demand profile 695 can then be stored in a database 680 for furtheranalysis. Although a blending model for juice is described, the blendingmodel can be applied to any agriculture-based product. The blendingmodel can be applied to from concentrate juice or not from concentratejuice.

As discussed above, the each valid blending plan 665 can be processed bythe liking profiler 670. The liking profiler 670 can be a model ofconsumer liking based on the attributes of a product. In oneillustrative embodiment, the liking profiler 670 can be amulti-dimensional mathematical model that associates a liking score withthe brix, citric acid, brix acid ratio, centrifuge pulp profile, vitaminc (ascorbic acid), percent recovered oil, color score, defects score,limonin, and flavor score of a product. More or fewer attributes can beincluded. The attributes can be weighted. The liking score can be arelative value. The liking score can be a scalar, vector, or randomvariable. The multi-dimensional mathematical model can be populated withdata from consumer surveys and consumer purchase information. Themulti-dimensional mathematical model is a compilation of these datadescribing consumers. For a given brix, citric acid, brix acid ratio,centrifuge pulp profile, vitamin c (ascorbic acid), percent recoveredoil, color score, defects score, limonin, and flavor score of a product,the multi-dimensional mathematical model can produce a liking score. Theliking profiler 670 will score each product in the blending plan 665.The liking profiler 670 returns the liking profile 675 which can consistof the liking score for each product in the blending plan 665.Alternatively, the liking profiler 670 can return a liking score foreach SKU.

The demand module 690 can generate the demand profile 695 for theblending plan 665 based on the liking profile 675. The demand module 690can include a demand model of likely demand based on consumer liking ofthe attributes of products to be released into a market and the totalvolume and form of the products to be released into the market. Thedemand model can be a multi-dimensional mathematical model orstatistical model that associates a liking score with historicalpurchase data. A demand curve can be generated for each product or SKUof the blending plan 665. The demand model can account for cannibalismamongst the products or SKUs based on the volume or units producedaccording to the blending plan 665. The volume or units produced foreach product or SKU according to the blending plan 665 can be used tocalculate a proposed price for the a product on the demand curve. Thedemand profile 695 can include the demand curve for each product and aproposed price for each product.

The demand module 690 can then calculate the profit of the blending plan665 at various price points using the demand profile 695 and the coststructure information of the optimal solution 667 for the blending plan665. Using a system of equations for each product or SKU, such asobjective functions and constraint functions described above, the demandmodule 690 can maximize the profit by testing various price scenariosagainst the demand profile 695.

In addition, after many runs of the blending model 660, the demandmodule 690 can use the volume or units produced according to a pluralityof blending plans 665 along with the cost structure information of theoptimal solutions 667 of the plurality of blending plans 665 to generatea supply curve for a company. In other words, the blending model 660 canbuild a supply curve based on the (minimized) cost, or price, ofproviding a particular volume of product. Each of the blending plans 665can provide a data point for generating the supply curve. Alternatively,the supply curve can be constructed using prior blending plans 765stored in the database 780.

The blending plan 665 and its optimal solution 667, liking profile 675,and demand profile 695 can be stored in a database 680 for display orfurther analysis. The results of the analysis can be interactivelydisplayed. For example, a graph showing the demand curves for eachproduct or SKU of the demand profile 695 can be presented to a user suchas a business unit manager. A graph showing the supply curve eachproduct or SKU can also be presented. The user can change the variousattributes, constraints, cost structures and resources available tosimulate how changes will effect the supply and demand of products ofthe drink production system. In addition, a user can choose variousprice points to manipulate to see how different prices will affectprofitability. Sensitivity analyses can include automatically generatingscenarios where attributes, constraints, cost structures and resourcesare changed by a percentage, for example, ten percent.

Advantageously, the blending model architecture can provide supply anddemand data and a common communication platform to enablecross-functional coordination to enhance blending decisions.Advantageously, the blending model architecture can efficiently analyzemultiple scenarios, vary demand, raw material attributes, and costs at agranular level to evaluate trade-offs and execute strategy.Advantageously, users can interact with various blending plans 665 tobetter understand possible supply and demand scenarios.

Referring to FIG. 7, a diagram of a blending model architecture 700 inaccordance with an illustrative embodiment is shown. The blending modelarchitecture 700 can include a blending model 760, as discussed above.Inputs to the blending model 760 can include a forecast, inventoryinformation, production information, channel information, and desiredattributes, as described above. The blending model 760 generates ablending plan 765 and an optimal solution 767 based on the inputs. Aliking profiler 770 provides a liking profile 775 for the blending plan765. The blending plan 765 and its optimal solution 767 and likingprofile 775 can then be provided to a demand module 790. The demandmodule 790 can generate a demand profile 795 for the blending plan 765.The blending plan 765 and its optimal solution 767, liking profile 775and demand profile 795 can then be provided to a promotion module 705along with a promotion query 702. The promotion module 705 can generatea promotion plan 707 for the blending plan 765. The blending plan 765and its optimal solution 767, liking profile 775, demand profile 795,promotion plan 707 can then be stored in a database 780 for furtheranalysis. Although a blending model for juice is described, the blendingmodel can be applied to any agriculture-based product. The blendingmodel can be applied to from concentrate juice or not from concentratejuice.

As discussed above, the each valid blending plan 765 can be processed bythe liking profiler 770. The liking profiler 770 can be a model ofconsumer liking based on the attributes of a product. In oneillustrative embodiment, the liking profiler 770 can be amulti-dimensional mathematical model that associates a liking score withvarious attributes of a product. The multi-dimensional mathematicalmodel can be populated with data from consumer surveys and consumerpurchase information. The multi-dimensional mathematical model is acompilation of these data describing consumers. For a given attributemix of a product, the multi-dimensional mathematical model can produce aliking score. The liking profiler 770 will score each product in theblending plan 765. The liking profiler 770 returns the liking profile775 which can consist of the liking score for each product in theblending plan 765. Alternatively, the liking profiler 770 can return aliking score for each SKU.

The demand module 790 can generate the demand profile 795 for theblending plan 765 based on the liking profile 775. The demand module 790can include a demand model of likely demand based on consumer liking ofthe attributes of products to be released into a market and the totalvolume and form of the products to be released into the market. Thedemand model can be a multi-dimensional mathematical model orstatistical model that associates a liking score with historicalpurchase data. A demand curve can be generated for each product or SKUof the blending plan 765. The demand model can account for cannibalismamongst the products or SKUs based on the volume or units producedaccording to the blending plan 765. The volume or units produced foreach product or SKU according to the blending plan 765 can be used tocalculate a proposed price for the a product on the demand curve. Thedemand profile 795 can include the demand curve for each product and aproposed price for each product.

In addition, after many runs of the blending model 760, the demandmodule 790 can use the volume or units produced according to a pluralityof blending plans 765 along with the cost structure information of theoptimal solutions 767 of the plurality of blending plans 765 to generatea supply curve for a company. Each of the blending plans 765 can providea data point for generating the supply curve.

The promotion module 705 can test promotion scenarios by manipulatingconstraints of the blending model 760 or by mining blending plans 765previously stored in the database 780. The promotion module 705 canreceive the promotion query 702. The promotion query 702 providesconstraints or restrictions related to how to deploy a promotion. Thepromotion query 702 can be a lump sum of promotion money or a targetedsum of promotion money. Likewise, other promotions, for example,coupons, toys, free samples, etc., can be simulated as promotion money.For example, the promotion query 702 can be directed to finding the mostprofitable way to spend two hundred thousand dollars of promotion money.In another example, the promotion query 702 can be directed todetermining the effect of spending two hundred thousand dollars on thepromotion of a specific product.

In an illustrative embodiment, the effect of the promotion can bemodeled as reducing the cost structure of the inputs of a product. Thepromotion module 705 can manipulate current constraints and introducenew constraints to the blending model 760. The promotion plan 707 caninclude the set of new constraints and changes to the currentconstraints. For example, promotion module 705 can direct the blendingmodel 760 to reduce the input costs of product ‘X’ by ten cents/gallonand create another constraint that states that the number of gallons ofproduct ‘X’ times ten cents cannot exceed two hundred thousand dollars.The blending model 760 can be iterated until valid blending plans 765are found that satisfy the new promotion constraints. Variousconstraints can be employed to simulate target promotions. When a validblending model 760 is found, the promotion plan 707 can be stored as avalid promotion plan 707. The valid promotion plan 707 can the be usedby a business manager for implementing a promotion campaign.

Alternatively, the promotion module 705 can instruct the demand module790 to use a specific price for a target product during analysis. Thedemand module 790 can determine the maximum profit without the promotionand with the promotion. The promotion module 705 can force the blendingmodel to generate valid blending plans 765 until a maximum profit isdetermined in the situation with the promotion, but where the differencein the maximum profit without the promotion and with the promotion isequal to the promotion amount. In one illustrative embodiment, randomblend plans can be injected into the liking profiler to promotediscovery of valid blending plans 765. The promotion plan 707 can bederived based on the differences between the specific price for thetarget product and the proposed price for the target product calculatedby the demand module 790.

The blending plan 765 and its optimal solution 767, liking profile 775,demand profile 795, promotion query 702, and promotion plan 707 can bestored in a database 780 for display or further analysis. The results ofthe analysis can be interactively displayed. For example, a graph ortable showing possible promotion query 702 and promotion plan 707 setscan be presented to a user such as a business unit manager. The user canchange the various attributes, constraints, cost structures andresources available to simulate how changes will effect the promotion.Sensitivity analyses can include automatically generating scenarioswhere attributes, constraints, cost structures and resources are changedby a percentage, for example, ten percent.

Advantageously, the blending model architecture can provide promotiondata and a common communication platform to enable cross-functionalcoordination to enhance blending decisions. Advantageously, the blendingmodel architecture can efficiently analyze multiple promotion scenarios,vary demand, raw material attributes, and costs at a granular level toevaluate trade-offs and execute strategy. Advantageously, users caninteract with various blending plans 765 to better understand possiblepromotion scenarios.

One or more flow diagrams may have been used herein. The use of flowdiagrams is not meant to be limiting with respect to the order ofoperations performed. The herein described subject matter sometimesillustrates different components contained within, or connected with,different other components. It is to be understood that such depictedarchitectures are merely exemplary, and that in fact many otherarchitectures can be implemented which achieve the same functionality.In a conceptual sense, any arrangement of components to achieve the samefunctionality is effectively “associated” such that the desiredfunctionality is achieved. Hence, any two components herein combined toachieve a particular functionality can be seen as “associated with” eachother such that the desired functionality is achieved, irrespective ofarchitectures or intermedial components. Likewise, any two components soassociated can also be viewed as being “operably connected”, or“operably coupled”, to each other to achieve the desired functionality,and any two components capable of being so associated can also be viewedas being “operably couplable”, to each other to achieve the desiredfunctionality. Specific examples of operably couplable include but arenot limited to physically mateable and/or physically interactingcomponents and/or wirelessly interactable and/or wirelessly interactingcomponents and/or logically interacting and/or logically interactablecomponents.

With respect to the use of substantially any plural and/or singularterms herein, those having skill in the art can translate from theplural to the singular and/or from the singular to the plural as isappropriate to the context and/or application. The varioussingular/plural permutations may be expressly set forth herein for sakeof clarity.

It will be understood by those within the art that, in general, termsused herein, and especially in the appended claims (e.g., bodies of theappended claims) are generally intended as “open” terms (e.g., the term“including” should be interpreted as “including but not limited to,” theterm “having” should be interpreted as “having at least,” the term“includes” should be interpreted as “includes but is not limited to,”etc.). It will be further understood by those within the art that if aspecific number of an introduced claim recitation is intended, such anintent will be explicitly recited in the claim, and in the absence ofsuch recitation no such intent is present. For example, as an aid tounderstanding, the following appended claims may contain usage of theintroductory phrases “at least one” and “one or more” to introduce claimrecitations. However, the use of such phrases should not be construed toimply that the introduction of a claim recitation by the indefinitearticles “a” or “an” limits any particular claim containing suchintroduced claim recitation to inventions containing only one suchrecitation, even when the same claim includes the introductory phrases“one or more” or “at least one” and indefinite articles such as “a” or“an” (e.g., “a” and/or “an” should typically be interpreted to mean “atleast one” or “one or more”); the same holds true for the use ofdefinite articles used to introduce claim recitations. In addition, evenif a specific number of an introduced claim recitation is explicitlyrecited, those skilled in the art will recognize that such recitationshould typically be interpreted to mean at least the recited number(e.g., the bare recitation of “two recitations,” without othermodifiers, typically means at least two recitations, or two or morerecitations). Furthermore, in those instances where a conventionanalogous to “at least one of A, B, and C, etc.” is used, in generalsuch a construction is intended in the sense one having skill in the artwould understand the convention (e.g., “a system having at least one ofA, B, and C” would include but not be limited to systems that have Aalone, B alone, C alone, A and B together, A and C together, B and Ctogether, and/or A, B, and C together, etc.). In those instances where aconvention analogous to “at least one of A, B, or C, etc.” is used, ingeneral such a construction is intended in the sense one having skill inthe art would understand the convention (e.g., “a system having at leastone of A, B, or C” would include but not be limited to systems that haveA alone, B alone, C alone, A and B together, A and C together, B and Ctogether, and/or A, B, and C together, etc.). It will be furtherunderstood by those within the art that virtually any disjunctive wordand/or phrase presenting two or more alternative terms, whether in thedescription, claims, or drawings, should be understood to contemplatethe possibilities of including one of the terms, either of the terms, orboth terms. For example, the phrase “A or B” will be understood toinclude the possibilities of “A” or “B” or “A and B.”

The foregoing description of illustrative embodiments has been presentedfor purposes of illustration and of description. It is not intended tobe exhaustive or limiting with respect to the precise form disclosed,and modifications and variations are possible in light of the aboveteachings or may be acquired from practice of the disclosed embodiments.It is intended that the scope of the invention be defined by the claimsappended hereto and their equivalents.

What is claimed is:
 1. A system for optimizing blending comprising: aprocessor configured to: aggregate material information for massproduction of at least one product, wherein the material information isassociated with a product input of at least one product; aggregateproduction information, wherein the production information is associatedwith production resources of the at least one product; aggregateconsumer liking information of the at least one product; establishobjective functions; establish constraint functions; model consumerliking based on the aggregated consumer liking information of the atleast one product; calculate a blending plan as a function of theaggregated material information, production information, and modeledconsumer liking, and in calculating the blending plan, utilize theobjective and constraint functions; and provide blending planinformation based on the blending plan for controlling the productionresources; wherein the system optimizing the blending plan receives orsends data on one or more factors selected from the group consisting of:(a) obtaining beverage input supply information; (b) obtaininginventory; (c) obtaining orders from channels; (d) managing inventory;(e) controlling production resources; (f) producing cost data; (g)producing quality data; (h) evaluating multiple alternative raw materialand cost scenarios; (i) predicting consumer demand; (j) forecastinginventory; (k) forecasting product availability; (l) forecastingproduction availability; (m) forecasting brix; and (n) forecasting pulpprofile.
 2. The system of claim 1, wherein the product input comprisesan agricultural commodity.
 3. The system of claim 2, wherein theagricultural commodity comprises oranges.
 4. The system of claim 1,wherein the material information comprises at least one of brix,acidity, limonin, nomilin, color, mouth-feel, pulp content profile,cost, freight cost, storage cost, and quality.
 5. The system of claim 1,wherein the production information comprises at least one of machineavailability, vat availability, storage availability; machine turnovertime; machine turnover cost, labor information, plant capacity, andmachine capacity.
 6. The system of claim 1, wherein modeling consumerliking of the at least one product comprises determining a demanddistribution based on consumer data comprising at least one of consumerpurchase data and consumer survey data.
 7. The system of claim 1,wherein the at least one product comprises at least one stock keepingunit.
 8. The system according to claim 1, wherein said processor isfurther configured to solve for at least one variable utilizing a systemof linear equations used to describe the objective and constraintfunctions when calculating the blending plan.
 9. A method of optimizingblending comprising: aggregating, at a processor, material informationfor mass production of at least one product, wherein the materialinformation is associated with a product input of at least one product;aggregating, by the processor, production information, wherein theproduction information is associated with production resources of the atleast one product; aggregating, by the processor, consumer likinginformation of the at least one product; establishing objectivefunctions; establishing constraint functions; modeling, by theprocessor, consumer liking based on the aggregated consumer likinginformation of the at least one product; calculating, by the processor,a blending plan as a function of the aggregated material information,production information, and modeled consumer liking, and in calculatingthe blending plan, utilizing the objective and constraint functions; andproviding, by the processor, blending plan information based on theblending plan for controlling the production resources; whereinoptimizing blending comprises receiving or sending data on one or morefactors selected from the group consisting of: (a) obtaining beverageinput supply information; (b) obtaining inventory; (c) obtaining ordersfrom channels; (d) managing inventory; (e) controlling productionresources; (f) producing cost data; (g) producing quality data; (h)evaluating multiple alternative raw material and cost scenarios; (i)predicting consumer demand; (j) forecasting inventory; (k) forecastingproduct availability; (l) forecasting production availability; (m)forecasting brix; and (n) forecasting pulp profile.
 10. The method ofclaim 9, wherein the material information comprises at least one ofbrix, acidity, limonin, nomilin, color, mouth-feel, pulp contentprofile, cost, freight cost, storage cost, and quality.
 11. The methodof claim 9, wherein the production information comprises at least one ofmachine availability, vat availability, storage availability; machineturnover time; machine turnover cost, labor information, plant capacity,and machine capacity.
 12. The method of claim 9, wherein modelingconsumer liking of the at least one product comprises determining ademand distribution based on consumer data comprising at least one ofconsumer purchase data and consumer survey data.
 13. The method of claim9, wherein the at least one product comprises at least one stock keepingunit.
 14. The method of claim 9, wherein the product input comprises anagricultural commodity.
 15. An article of manufacture including atangible computer-readable medium having instructions stored thereonthat, if executed by a computing device, cause the computing device toperform operations comprising: aggregating material information for massproduction of at least one product, wherein the material information isassociated with a product input of at least one product; aggregatingproduction information, wherein the production information is associatedwith production resources of the at least one product; aggregateconsumer liking information of the at least one product; establishingobjective functions; establishing constraint functions; modelingconsumer liking based on the aggregated consumer liking information ofthe at least one product; calculating a blending plan as a function ofthe aggregated material information, production information, and modeledconsumer liking, and in calculating the blending plan, utilizing theobjective and constraint functions; and providing blending planinformation based on the blending plan for controlling the productionresources; wherein the operations are selected from the group consistingof: (a) obtaining beverage input supply information; (b) obtaininginventory; (c) obtaining orders from channels; (d) managing inventory;(e) controlling production resources; (f) producing cost data; (g)producing quality data; (h) evaluating multiple alternative raw materialand cost scenarios; (i) predicting consumer demand; (j) forecastinginventory; (k) forecasting product availability; (l) forecastingproduction availability; (m) forecasting brix; and (n) forecasting pulpprofile.
 16. The article of manufacture of claim 15, wherein thematerial information comprises at least one of brix, acidity, limonin,nomilin, color, mouth-feel, pulp content profile, cost, freight cost,storage cost, and quality.
 17. The article of manufacture of claim 15,wherein the production information comprises at least one of machineavailability, vat availability, storage availability; machine turnovertime; machine turnover cost, labor information, plant capacity, andmachine capacity.
 18. The article of manufacture of claim 15, whereinmodeling consumer liking of the at least one product comprisesdetermining a demand distribution based on consumer data comprising atleast one of consumer purchase data and consumer survey data.
 19. Thearticle of manufacture of claim 15, wherein the product input comprisesan agricultural commodity and the at least one product comprises atleast one stock keeping unit.